The Application: A Microservices E-Commerce App
The project is built around a real-world microservices-based e-commerce application — seven independent services, each containerized and independently deployable.
E-Commerce Microservices
┌────────────────────────────────────────┐
│ - Frontend (UI) │
│ - Cart Service │
│ - Orders Service │
│ - Checkout Service │
│ - Payments Service │
│ - Product Catalog Service │
│ - Recommendation Service │
└────────────────────────────────────────┘
Each service is isolated, owns its own responsibility, and communicates over well-defined APIs — mirroring how teams actually build and ship software at scale.
The Full Architecture: End-to-End Flow
Developer pushes code
↓
GitHub (GitOps — Source of Truth)
↓
CI/CD Pipeline (GitHub Actions)
┌──────────────────────────────────────┐
│ - Run tests │
│ - Build Docker images │
│ - Push to container registry │
│ - Update Kubernetes manifests │
└──────────────────────────────────────┘
↓
Argo CD (GitOps Continuous Delivery)
Watches Git repo → syncs cluster state
↓
AWS EKS Cluster (Terraform-provisioned)
┌──────────────────────────────────────┐
│ Microservices on Kubernetes │
│ - Cart - Orders │
│ - Checkout - Payments │
│ - Catalog - Frontend │
│ - Recommendations │
└──────────────────────────────────────┘
↓
Observability Stack
┌──────────────────────────────────────┐
│ Prometheus → Metrics collection │
│ Grafana → Dashboards & alerts │
│ Loki → Log aggregation │
└──────────────────────────────────────┘
↓
AIOps Layer
┌──────────────────────────────────────┐
│ - Anomaly Detection │
│ - Intelligent Log Analysis │
│ - Auto-remediation │
│ - Incident Response Automation │
└──────────────────────────────────────┘
Layer 1: Local Development with Docker Compose
All seven microservices run locally using Docker Compose — spin up the full app on any laptop with a single command, no cloud credentials required. This validates the application before any infrastructure costs are incurred.
Layer 2: Infrastructure as Code with Terraform
AWS infrastructure is never clicked together manually. Terraform declares it as code — repeatable, version-controlled, and auditable.
Terraform provisions on AWS
┌──────────────────────────────────────┐
│ EKS Cluster │
│ VPC + Subnets + Security Groups │
│ IAM Roles & Policies │
│ Node Groups (EC2 worker nodes) │
│ Load Balancers │
└──────────────────────────────────────┘
Layer 3: CI/CD Pipeline with GitHub Actions
Every code push triggers an automated pipeline:
Code pushed to GitHub
↓
┌──────────────────────────────────────┐
│ 1. Run unit & integration tests │
│ 2. Build Docker image │
│ 3. Push image to container registry │
│ 4. Update image tag in K8s manifests│
│ 5. Commit updated manifests to Git │
└──────────────────────────────────────┘
↓
Argo CD detects the change
Layer 4: GitOps with Argo CD
Git is the single source of truth. Argo CD continuously watches the repo and auto-syncs the live cluster to match the declared state — self-healing, auditable, and rollbacks are just a git revert away.
Git Repository (Desired State)
↓ Argo CD watches for drift
AWS EKS Cluster (Actual State)
↓
Drift detected → Auto-sync to reconcile
Layer 5: Kubernetes on AWS EKS
Amazon EKS manages the Kubernetes control plane so the team focuses on workloads, not cluster maintenance.
AWS EKS Cluster
┌──────────────────────────────────────────────┐
│ Deployments → Run & manage pods │
│ Services → Internal/external routing │
│ Ingress → External traffic entry │
│ ConfigMaps → App configuration │
│ Secrets → Sensitive credentials │
│ HPA → Horizontal Pod Autoscaling │
└──────────────────────────────────────────────┘
Layer 6: Observability — Prometheus, Grafana & Loki
Prometheus → Scrapes & stores metrics (CPU, memory, req/s, errors)
↓
Grafana → Visualizes metrics (dashboards + alerting)
↓
Loki → Aggregates logs from all microservices
Together they provide full visibility into application health, resource usage, error rates, and logs — all in one place.
Layer 7: AIOps — Intelligent Operations
AIOps moves beyond passive monitoring toward autonomous operations using ML and LLMs.
Raw Telemetry (Metrics + Logs + Traces)
↓
AIOps Layer
┌──────────────────────────────────────────────┐
│ Anomaly Detection │
│ → Flags issues before users are impacted │
│ │
│ Intelligent Log Analysis │
│ → LLMs parse & summarize logs │
│ → Pinpoints root cause faster │
│ │
│ Auto-Remediation │
│ → Auto-scales pods, restarts containers │
│ → Triggers rollbacks on degraded deploys │
│ │
│ Incident Response Automation │
│ → Notifies on-call with context, not noise │
└──────────────────────────────────────────────┘
Tools & Technologies
| Category | Tool |
|---|---|
| Containerization | Docker, Docker Compose |
| Orchestration | Kubernetes (AWS EKS) |
| Infrastructure as Code | Terraform |
| CI/CD | GitHub Actions |
| GitOps | Argo CD |
| Metrics | Prometheus |
| Dashboards & Alerts | Grafana |
| Log Aggregation | Loki |
| Cloud Provider | AWS |
| AIOps | ML anomaly detection + LLM log analysis |
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